Risk-Tiered Governance for Hospital Artificial Intelligence: A Framework Synthesis and Implementation Pathway

OBJECTIVES: Hospital artificial intelligence (AI) is increasingly embedded in electronic health record workflows, cloud inference pipelines, imaging, medication review, triage, early warning, documentation, and operational management. This paper proposes a risk-tiered governance framework and implementation pathway for hospital AI applications that are integrated into clinical and operational workflows. METHODS: We conducted a narrative review and framework synthesis of peer-reviewed evidence, reporting guidelines, regulatory and policy sources, implementation studies, and applied governance case reports relevant to hospital AI. Sources were used according to their evidence type: systematic and scoping reviews identified recurring risks and barriers; empirical studies and trials illustrated bounded implementation patterns; case reports informed organizational design; and reporting or regulatory frameworks informed documentation, validation, change control, and oversight elements. RESULTS: The proposed framework has four components. First, a use-case inventory tags AI applications by decision influence and workflow coupling. Second, a six-domain risk taxonomy addresses clinical safety, privacy and data security, ethics and fairness, transparency and explainability, system stability and resilience, and compliance and accountability. Third, a four-tier risk scheme links the level of oversight to potential harm, automation, reversibility, and operational coupling. Fourth, a governance architecture assigns responsibilities to an AI governance committee, clinical owners, risk-control functions, and independent assurance. A patient-safety-oriented lifecycle pathway is proposed across initiation, local validation, shadow mode, controlled go-live, monitoring, change control, retirement, and organizational learning. To support feasible adoption, we also provide barriers, first steps, and a maturity-based ramp-up model. CONCLUSION: Hospital AI governance is best framed as proportional lifecycle control rather than one-time go-live approval. The proposed framework is an adaptable scaffold, not a universal mandate. It is intended to help clinical informatics leaders, quality and safety teams, and hospital executives prioritize oversight according to risk, resources, and organizational maturity.